The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed from modern machines, such as Magnetic Resonance (MR) imaging scanners, Computed Tomographic (CT) scanners and Positron Emission Tomographic (PET) scanners, to multimodality imaging systems such as PET-CT and PET-MRI systems. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a computerized axial tomography (CAT) scanner, magnetic resonance imaging (MRI), etc. Digital medical images are typically either a two-dimensional (“2D”) image made of pixel elements, a three-dimensional (“3D”) image made of volume elements (“voxels”) or a four-dimensional (“4D”) image made of dynamic elements (“doxels”). Such 2D, 3D or 4D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities or pathologies, such as cysts, tumors, polyps, aneurysms, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image are generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images, localize and segment structure of interests, including possible abnormalities (or candidates), for further review. Recognizing structures of interest within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of structures of interest within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor in order to reach an early diagnosis.
CAD may be used to facilitate diagnosis of aortic aneurysms and guide therapeutic decisions. Aortic aneurysm is one of the top fifteen causes of death. An aortic aneurysm is an abnormal bulge within the wall of the aorta. The aorta is the largest artery in the body that carries blood from the heart to the body. Aortic aneurysms may occur anywhere in the aorta. An abdominal aortic aneurysm occurs along a portion of the aorta that passes through the abdomen, while a thoracic aortic aneurysm occurs along a portion of the aorta that passes through the chest cavity. Having an aortic aneurysm increases the risk of developing an aortic dissection or rupture, which can be fatal. Early and accurate diagnosis is key to reducing risk of death.